Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, 95616, USA.
Nat Commun. 2018 Feb 9;9(1):606. doi: 10.1038/s41467-018-02923-8.
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies.
RNA 在多种细胞过程中发挥关键的调节作用，其功能通常源自于折叠成不同的结构并在这些结构之间转换。许多 RNA 进一步依赖于多种结构的共存，这些结构控制着它们对细胞信号的反应。然而，无论是在实验上还是计算上，对异质结构景观进行特征描述都是很困难的。最近，结构分析实验已经成为强大且经济实惠的结构特征描述方法，它们可以改进计算结构预测。迄今为止，人们的工作重点主要是预测一个最优结构，而在多结构预测方面则进展较少。在这里，我们报告了一种概率建模方法，该方法可以预测一组简洁的共存结构，并从结构分析数据中估计它们的丰度。我们通过分析大量数据集，展示了稳健的景观重建和对结构动力学的定量见解。这项工作为基于数据的结构景观特征描述建立了一个框架，以帮助实验人员进行结构功能研究。
